System And Method For Interactive Discovery Of Inter-Data Set Relationships

ABSTRACT

Provided are methods and systems comprising determining one or more relationships between a plurality of data sets, determining a score for each of the one or more relationships, generating a graphical data set object for each of the plurality of data sets, classifying each graphical data set object as connected or unconnected based on the score for each of the one or more relationships, generating a graphical connector object between connected graphical data set objects, and outputting the connected graphical data set objects with corresponding graphical connector objects and the unconnected graphical data set objects.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/329,657 filed Apr. 29, 2016, herein incorporated by reference in itsentirety.

SUMMARY

It is to be understood that both the following general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive, as claimed. Provided are methods and systemsfor data management and analysis.

In an aspect, provided is a method comprising determining one or morerelationships between a plurality of data sets, determining a score foreach of the one or more relationships, generating a graphical data setobject for each of the plurality of data sets, generating one or moregraphical connector objects, wherein the one or more graphical connectorobjects connect one of more of the graphical data set objects based onthe determined one or more relationships with a corresponding scoreabove a threshold, applying a force-directed graph layout algorithm todetermine a position for each graphical data set object based on thescores for each of the one or more relationships, applying theforce-directed graph layout algorithm to determine a distance betweeneach of the graphical data set objects based on the scores for each ofthe one or more relationships, and outputting the graphical data setobjects and the one or more graphical connector objects based on thedetermined positions and distances.

In another aspect, provided is a method comprising determining one ormore relationships between a plurality of data sets, determining a scorefor each of the one or more relationships, generating a graphical dataset object for each of the plurality of data sets, classifying eachgraphical data set object as connected or unconnected based on the scorefor each of the one or more relationships, generating a graphicalconnector object between connected graphical data set objects, andoutputting the connected graphical data set objects with correspondinggraphical connector objects and the unconnected graphical data setobjects.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1A is a schematic diagram showing an embodiment of a system formingan implementation of the disclosed methods;

FIG. 1B illustrates an example user interface;

FIG. 1C illustrates an example process for generating the userinterface;

FIG. 1D illustrates attraction forces applied to graphical data setobjects;

FIG. 1E illustrates a dynamic disclosure of possible relationshipsbetween data sets;

FIG. 1F illustrates an interactive phase when a user moves a graphicaldata set object over another graphical data set object;

FIG. 1G illustrates an interactive phase when the user discovers arelationship between data sets;

FIG. 2 is a set of tables showing exemplary Tables 1-5 of a simpledatabase and associations between variables in the tables;

FIG. 3 is a schematic flowchart showing basic steps performed whenextracting information from a database;

FIG. 4 is tables showing a final data structure, e.g. a multidimensionalcube, created by evaluating mathematical functions;

FIG. 5A is a schematic diagram showing how a selection by a useroperates on a scope to generate a data subset;

FIG. 5B is an overview of the relations between data model, indexes indisk and windowed view of disk indexes in memory;

FIG. 5C is a representation of the data structure used for tablehandling;

FIG. 5D is a table tree representation of a data model;

FIG. 5E illustrates an example application of bidirectional tableindexes and bidirectional association indexes;

FIG. 6 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received afterprocessing by an external engine;

FIG. 7 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 6;

FIG. 8 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after secondcomputations from an external engine;

FIG. 9 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 8;

FIG. 10 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after thirdcomputations from an external engine;

FIG. 11 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 10;

FIG. 12 is a table showing results from computations based on differentselections in the presentation of FIG. 10;

FIG. 13 is a schematic graphical presentation showing a further set ofselections and a diagram of data associated to the selections asreceived after third computations from an external engine;

FIG. 14 is a flow chart illustrating an example method;

FIG. 15 is a flow chart illustrating another example method; and

FIG. 16 is an exemplary operating environment for performing thedisclosed methods.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described inmore detail, it is to be understood that the methods and systems are notlimited to specific steps, processes, components, or structuredescribed, or to the order or particular combination of such steps orcomponents as described. It is also to be understood that theterminology used herein is for the purpose of describing exemplaryembodiments only and is not intended to be restrictive or limiting.

As used herein the singular forms “a,” “an,” and “the” include bothsingular and plural referents unless the context clearly dictatesotherwise. Values expressed as approximations, by use of antecedentssuch as “about” or “approximately.” shall include reasonable variationsfrom the referenced values. If such approximate values are included withranges, not only are the endpoints considered approximations, themagnitude of the range shall also be considered an approximation. Listsare to be considered exemplary and not restricted or limited to theelements comprising the list or to the order in which the elements havebeen listed unless the context clearly dictates otherwise.

Throughout the specification and claims of this disclosure, thefollowing words have the meaning that is set forth: “comprise” andvariations of the word, such as “comprising” and “comprises,” meanincluding but not limited to, and are not intended to exclude, forexample, other additives, components, integers or steps. “Exemplary”means “an example of”, but not essential, necessary, or restricted orlimited to, nor does it convey an indication of a preferred or idealembodiment. “Include” and variations of the word, such as “including”are not intended to mean something that is restricted or limited to whatis indicated as being included, or to exclude what is not indicated.“May” means something that is permissive but not restrictive orlimiting. “Optional” or “optionally” means something that may or may notbe included without changing the result or what is being described.“Prefer” and variations of the word such as “preferred” or “preferably”mean something that is exemplary and more ideal, but not required. “Suchas” means something that is exemplary.

Steps and components described herein as being used to perform thedisclosed methods and construct the disclosed systems are exemplaryunless the context clearly dictates otherwise. It is to be understoodthat when combinations, subsets, interactions, groups, etc. of thesesteps and components are disclosed, that while specific reference ofeach various individual and collective combinations and permutation ofthese may not be explicitly disclosed, each is specifically contemplatedand described herein, for all methods and systems. This applies to allaspects of this application including, but not limited to, steps indisclosed methods and/or the components disclosed in the systems. Thus,if there are a variety of additional steps that can be performed orcomponents that can be added, it is understood that each of theseadditional steps can be performed and components added with any specificembodiment or combination of embodiments of the disclosed systems andmethods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the Examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices,whether internal, networked or cloud based.

Embodiments of the methods and systems are described below withreference to diagrams, flowcharts and other illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

The present disclosure relates to computer implemented methods andsystems for data management, data analysis, and processing. Thedisclosed methods and systems can incorporate external data analysisinto an otherwise closed data analysis environment. A typicalenvironment for the systems and methods described herein is forassisting in a computer implemented method for building and updating amulti-dimensional cube data structure, such as, e.g., the systems andmethods described in U.S. Pat. Nos. 7,058,621; 8,745,099; 8,244,741; andU.S. pat. app. Ser. No. 14/054,321, which are incorporated by referencein their entireties.

In an aspect, the methods and systems can manage associations among datasets with every data point in the analytic dataset being associated withevery other data point in the dataset. Datasets can be larger thanhundreds of tables with thousands of fields. A multi-dimensional datasetor array of data is referred to as an OnLine Analytic Processing (OLAP)cube. A cube can be considered a multi-dimensional generalization of atwo- or three-dimensional spreadsheet. For example, it may be desired tosummarize financial data by product, by time-period, and by city tocompare actual and budget expenses. Product, time, city, and scenario(actual and budget) can be referred to as dimensions. Amulti-dimensional dataset is normally called a hypercube if the numberof dimensions is greater than 3. A hypercube can comprise tuples made oftwo (or more) dimensions and one or more expressions.

FIG. 1A illustrates an associative data indexing engine 100 with dataflowing in from the left and operations starting from a script engine104 and going clockwise (indicated by the clockwise arrow) to exportfeatures 118. Data from one or more data sources 102 can be extracted bya script engine 104. The data source 102 can comprise any type of knowndatabase, such as relational databases, post-relational databases,object-oriented databases, hierarchical databases, flat files, spreadsheet, etc. The Internet may also be regarded as a database in thecontext of the present disclosure. A user interface can be used as analternative or combined with a script engine 104. The script engine 104can read record by record from the data source 102 and data can bestored or appended to symbol and data tables in an internal database120. Read data can be referred to as a data set.

In an aspect, the user interface can be presented to a user to enablethe user to generate a data set from a plurality of data sets stored inthe data source 102 for the script engine 104 to extract and to be usedas the basis for a data model. The user interface can be configured toenable discovery of relationships between the data sets. Each data setcan be represented on a canvas (e.g., display) by a graphical object(e.g., a graphical data set object). The graphical data set object canbe any shape. By way of example, the graphical data set object can be acircle, a square, a triangle, and the like. Each graphical data setobject can be labeled with a data set name represented by the graphicaldata set object.

In an aspect, the data sets can have one or more relationships amongstthe data sets. As used herein, a relationship between two or more datasets can refer to a common attribute shared by the data sets, one ormore common values shared by the data sets, combinations thereof, andthe like. In some aspects, the data sets can have pre-establishedrelationships, such as tables in a database that have a primarykey-foreign key relationship. The pre-established relationships can berepresented to a user on the canvas (e.g., display) a shape connectingthe related data sets. In an aspect, the shape connecting related datasets (e.g., a graphical connector object) can be any shape, for example,one or more lines varying in thickness, color, curvature, and the like.FIG. 1B illustrates an example user interface 100 b. The user interface100 b can comprise a graphical data set object 122 connected to agraphical data set object 123 via a graphical connector object 124displayed on a canvas 125. The existence of the graphical connectorobject 124 between the graphical data set object 122 and the graphicaldata set object 123 illustrates to a user that a relationship existsbetween the graphical data set object 122 and the graphical data setobject 123. The graphical data set object 122 can represent underlyingData Set 1, whereas the graphical data set object 123 can representunderlying Data Set 2. The user interface 100 b can comprise a graphicaldata set object 126 that is not connected to another graphical data setobject. The graphical data set object 126 can represent underlying DataSet 3. In an aspect, a graphical data set object can be connected to aplurality of other graphical date set objects via a plurality ofgraphical connector objects.

The user interface 100 b can comprise a data set preview area 127. Thedata set preview area 127 can present the user with all or a portion ofa data set that underlies one of the graphical data set object 122, thegraphical data set object 123, or the graphical data set object 126based on a user selection of one of the graphical data set object 122,the graphical data set object 123, or the graphical data set object 126(e.g., by clicking on one of the graphical data set objects). The dataset preview area 127 can present the user with all or a portion of datainvolved in relationship between two or more graphical data objectsbased on a user selection (e.g., clicking) of a graphical connectorobject (e.g., the graphical connector object 124).

The user can select one of the graphical data set object 122, thegraphical data set object 123, or the graphical data set object 126 andmove (e.g., drag) the selected graphical data set object around thecanvas 125. The graphical data set objects can be configured to interactwith one another by exhibiting a gravity-like behaviour. For example,some graphical data set objects can be more attracted to some graphicaldata set objects than other graphical data set objects. As the usermoves a graphical data set object around the canvas 125, one or moregraphical connector objects can be deleted and/or created. For example,if the user selects the graphical data set object 123 and moves thegraphical data set object 123 away from the graphical data set object122, the graphical connector object 124 can be deleted. If the usermoves the graphical data set object 123 closer to the graphical data setobject 126, a new graphical connector object can be created between thegraphical data set object 123 and the graphical data set object 126 if arelationship can be established between the graphical data set object123 and the graphical data set object 126.

The methods and systems disclosed can thus guide the user in a discoveryprocess by providing visual information to the user. In an aspect, asize of graphical data set object can provide information about theunderlying data set. For example, the size of the graphical data setobject can be related to a number of rows in the corresponding data set.Positioning, distance, and gravity-like attraction between graphicaldata set objects (connected or unconnected) can provide informationabout the underlying data sets. For example, the closer two graphicaldata set objects are on the canvas 125 then the higher probability thata relationship exists between the two graphical data set objects.

FIG. 1C illustrates an example process for generating the user interface100 b. One or more data sources 128 a, 128 b, and 128 c can be accessedto extract one or more data sets 129 a, 129 b, and 129 c. The one ormore data sources 128 a, 128 b, and 128 c can comprise any type of knowndatabase, such as relational databases, post-relational databases,object-oriented databases, hierarchical databases, flat files, spreadsheet, etc. The Internet can also be regarded as a database in thecontext of the present disclosure. The one or more data sets 129 a. 129b, and 129 c can comprise all or a portion of the one or more datasources 128 a, 128 b, and 128 c. For example, the one or more data sets129 a, 129 b, and 129 c can comprise one or more database tables.

A Data Profiling and Classification Engine 130 can analyze the one ormore data sets 129 a, 129 b, and 129 c to determine one or morerelationships amongst the one or more data sets 129 a. 129 b, and 129 c.The Data Profiling and Classification Engine 130 can analyze thedetermined one or more relationships and determine a score for eachrelationship. A relationship R between two data sets DS1 and DS2 can bedefined by specifying collection of (one or more) fields (DS1.F1, . . .DS1.F_(n)) and (DS2.F2, . . . DS2.F_(k)) from each data set. A score ofa relationship R is a numeric value Score(R) that represents a qualityof the relationship. A score of zero indicates no relationship existsbetween two data sets, whereas a score greater than zero indicates theexistence of a relationship. Higher scores indicate higher qualityrelationships. The computation of the score can comprise aggregating oneor more parameters provided by the Data Profiling and ClassificationEngine 130: Score(R)=Aggregate(P1, P2, . . . Pn), where P_(k) is aprofiling/classification parameter. Examples of profiling/classificationparameter include, but are not limited to: ratios of unique values inthe fields to the total number of rows in the data set—high ratioindicates that the fields can be used as an identifier for the data set;ratios of the size of an overlapping set of values from fields on bothends of the association to the number of unique values in the datasets—higher ratio indicates better correlation of the data values;ratios of the size of an overlapping set of values from fields on bothends of the association to the total number of rows in the datasets—higher ratio indicates better correlation of the data values;history of user decisions made against the same or similar data;metadata supplied by external systems; hints and metadata supplied bydevelopers and administrators; classification score that identifiespotential lookup table; combinations thereof, and the like. An exampleof using the profiling results to calculate a score can be use theformula for the Aggregate(P1, P2 . . . Pn) which can be a linearfunction of the arguments: Score(R)=W1*P1+W2*P2+ . . . +Wn*Pn; whereWi—is a weight that specifies an importance of the profiling parameterPi, which also scale the profiling parameters values so the resultingvalue of the score falls in the range [0, 1]. A result of determining ascore for each relationship can comprise a list of recommendedrelationships for each pair of the one or more data sets 129 a, 129 b,and 129 c.

The Data Profiling and Classification Engine 130 can determine whichpairs of graphical data set objects can be displayed with a graphicalconnector object connecting the graphical data set objects (for example,in a default view or recommended view). In an aspect, the Data Profilingand Classification Engine 130 can compare each score to a thresholdvalue. Any relationship with a score exceeding the threshold value canbe determined to qualify for a graphical connector object between thecorresponding graphical data set objects. For scores ranging from 0 to1, the threshold can be, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,0.8, 0.9, or 1.

The determined scores can be used to enable simulated gravitationalforces between the graphical data set objects to attract graphical dataset objects that have higher relationship scores closer to each other.The Data Profiling and Classification Engine 130 can apply aforce-directed graph layout algorithm to position the graphical data setobjects on the canvas 125 based on calculated attraction forces. Thedirected-force layout algorithm can be an iterative process of applyingexisting forces between the graphical data set objects until thegravitational model is in a balance, meaning that the sum of all forcesapplied to each element (e.g., graphical data set objects) of the systemis equal to 0. Each element in the system can create a number ofattraction/repulsion forces for all other elements. These forces can becomputed from the relationship scores between data sets, for example,F(DS1, DS2)=W*Score(DS1, DS2)/Distance(DS1, DS2)², where W is a commonscaling factor to transform the score value into the range of forcevalues for a particular display size and where Distance(DS1, DS2) is thecurrent distance between center of the graphical data set objectsrepresenting data sets DS1 and DS1. The force could be negative whichmeans that the graphical elements do not have any relationships andshould move away from each other. At each step the force-directed graphlayout algorithm can calculate a new position for the graphical data setobjects, this new position is a delta shift in a direction of theresulting force which is a sum of all forces applied to the graphicalelement. NewPosition(DSk)=Position(DSk)+Sum(F(DSk, DSi) {for eachi})*StepSize; where StepSize is determined empirically to make forsmooth animation.

The Data Profiling and Classification Engine 130 can determine variousparameters for the generation and display of one or more graphical dataset objects and graphical connector objects to represent the underlyingdata sets and relationships amongst the same. A size of a graphical dataset object can be determined by applying logarithmic scaling of thenumber of the total rows in the data sets to make sure that the visiblesize of the graphical data set objects fall into a range betweenpredefined values—(minimum size, maximum size). The minimum size and themaximum size are selected so as to ensure that all graphical data setobjects are visible to a user.

The Data Profiling and Classification Engine 130 can output thedetermined graphical data set objects and graphical connector objects tothe user for interactive discovery based on the determined sizes of thegraphical data set objects, the determined positions of the graphicaldata set objects, the relationships determined to exceed a threshold,and the determined attraction forces.

FIG. 1C illustrates the graphical data set object 123 as a circle thatis larger than other displayed graphical data set objects 122 and 126.The user can surmise that the graphical data set object 123 containsmore data than the graphical data set objects 122 and 126. Examplescores are illustrated on the canvas 125 for the relationships betweenthe graphical data set objects 122, 123, 126. The graphical connectorobject 124 is shown connecting the graphical data set object 123 and thegraphical data set object 122 as a relationship between the twounderlying data sets has a score 131 of 0.8. A relationship betweenunderlying data sets for the graphical data set object 122 and thegraphical data set object 126 is illustrated as having a score 132 of0.1 and a relationship between underlying data sets for the graphicaldata set object 123 and the graphical data set object 126 is illustratedas having a score 133 of 0.8. The user interface can optionally displaythe values of the scores 131, 132, 133 to the user (for example, inresponse to a user interacting with one or more graphical data setobjects or graphical connector objects).

The positioning of the graphical data set object 123 close to thegraphical data set object 122 and the graphical data set object 126indicates to the user that relatively higher quality relationships existbetween the graphical data set object 123 and each of the graphical dataset object 122 and the graphical data set object 126. Whereas thegraphical data set object 122 and the graphical data set object 126 arepositioned relatively further away from one another, indicating arelatively lower quality relationship between the graphical data setobject 122 and the graphical data set object 126. In operation, the usercan drag the graphical data set object 123 closer to the graphical dataset object 126 until the graphical connector object 124 is deleted and anew graphical connector object is created that connects the graphicaldata set object 123 and the graphical data set object 126. In an aspect,a graphical data set object can be connected to a plurality of othergraphical date set objects via a plurality of graphical connectorobjects.

In an aspect, the existence or lack thereof of a graphical connectorobject between graphical data set objects can be referred to as aconnection state. The connection state can represent connections betweendata sets and lack of connections between data sets. Once the user issatisfied with a particular connection state, the methods and systemscan extract the underlying data sets and relationships for use increating a data model.

FIG. 1D illustrates attraction forces applied to the graphical data setobjects. Values of the attraction forces can be proportional to therelationship scores between the underlying data sets: F1˜Score(DS3,DS2), and F2˜Score(DS3, DS1). A higher relationship score for therelationship between Data Set 2 and Data Set 3 versus a lowerrelationship score for the relationship between Data Set 1 and Data Set3 results in a higher attraction force F1 between the graphical data setobject 123 and the graphical data set object 126. A lower relationshipscore for the relationship between Data Set 1 and Data Set 3 versus ahigher relationship score for the relationship between Data Set 2 andData Set 3 results in a lower attraction force FF between the graphicaldata set object 122 and the graphical data set object 126.

FIG. 1E illustrates a dynamic disclosure of possible relationshipsbetween data sets. A user can move a graphical data set object close toanother graphical data set object and in response, the non-moving (ormoving) graphical data set object can display a visual indication (e.g.,non-numeric) of a relationship score between the two underlying datasets. FIG. 1E illustrates a portion of a circle 135 displayed on thegraphical data set object 123. The circle 135 can be displayed in amanner that corresponds to the score of the relationship between DataSet 3 and Data Set 2. For example, the score of the relationship betweenData Set 3 and Data Set 2 can be 0.8 and the circle 135 can beillustrated as 80% complete. A full circle can thus indicate arelationship score of 1, a half circle can indicate a relationship scoreof 0.5, and absence of a circle can indicate a relationship score of 0.In other aspects, colors can be used to illustrate relationship scores.Other shapes besides a circle can be used to indicate relationship scoreto the user.

FIG. 1F illustrates an interactive phase when the user moves a graphicaldata set object over another graphical data set object which does nothave a recommended relationship to the moved graphical data set object.For example, as the user moves the graphical data set object 126 theother graphical data set objects can provide an indication ofrelationship scores with the Data Set 3. For example, the circle 135 canbe displayed on the graphical data set object 123 and a circle 136 canbe displayed on the graphical data set object 122. The circle 136 can bein a different color (e.g., red) that the circle 135 (e.g., green). Thered circle 136 can indicate to the user that no relationship existsbetween Data Set 1 and Data Set 3, or that the relationship that existshas a score that is below a threshold. Other shapes besides a circle andother colors can be used to indicate relationship score to the user.

FIG. 1G shows an example step in discovering a relationship when theuser drops the graphical data set object 126 for Data Set 3 onto thegraphical data set object 123 for Data Set 2 and creates a graphicalconnector object 137 illustrating the relationship between Data Set 3and Data Set 2. The result is that the graphical data set object 123 isconnected to graphical data set object 122 via graphical connectorobject 124 and is connected to graphical data set object 126 via newgraphical connector object 137.

In an aspect, a user can enter a query into the user interface 100 b.Any graphical data set object and/or graphical connector object that isrelated to the user query can be indicted to the user. For example, anygraphical data set object related to the query can be shown in adifferent color, can be positioned closer together (e.g., clustered),can be preferred to have graphical connector objects connecting thegraphical data set objects, and the like.

In an aspect, the extraction of the data can comprise extracting aninitial data set or scope from the data source 102, e.g. by reading theinitial data set into the primary memory (e.g. RAM) of the computer. Thedata extracted can comprise the one or more data sets selected by theuser via the user interface 100 b. The initial data set can comprise theentire contents of the data source 102 base, or a subset thereof. Theinternal database 120 can comprise the extracted data and symbol tables.Symbol tables can be created for each field and, in one aspect, can onlycontain the distinct field values, each of which can be represented bytheir clear text meaning and a bit filled pointer. The data tables cancontain said bit filled pointers.

In the case of a query of the data source 102, a scope can be defined bythe tables included in a SELECT statement (or equivalent) and how theseare joined. In an aspect, the SELECT statement can be SQL (StructuredQuery Language) based. For an Internet search, the scope can be an indexof found web pages, for example, organized as one or more tables. Aresult of scope definition can be a data set.

Once the data has been extracted, a user interface can be generated tofacilitate dynamic display of the data. By way of example, a particularview of a particular dataset or data subset generated for a user can bereferred to as a state space or a session. The methods and systems candynamically generate one or more visual representations of the data topresent in the state space.

A user can make a selection in the data set, causing a logical inferenceengine 106 to evaluate a number of filters on the data set. In anaspect, a query on a database that holds data of placed orders, could berequesting results matching an order year of ‘1999’ and a client groupbe ‘Nisse.’ The selection may thus be uniquely defined by a list ofincluded fields and, for each field, a list of selected values or, moregenerally, a condition. Based on the selection, the logical inferenceengine 106 can generate a data subset that represents a part of thescope. The data subset may thus contain a set of relevant data recordsfrom the scope, or a list of references (e.g. indices, pointers, orbinary numbers) to these relevant data records. The logical inferenceengine 106 can process the selection and can determine what otherselections are possible based on the current selections. In an aspect,flags can enable the logical inference engine 106 to work out thepossible selections. By way of example, two flags can be used: the firstflag can represent whether a value is selected or not, the second canrepresent whether or not a value selection is possible. For every clickin an application, states and colors for all field values can becalculated. These can be referred to as state vectors, which can allowfor state evaluation propagation between tables.

The logical inference engine 106 can utilize an associative model toconnect data. In the associative model, all the fields in the data modelhave a logical association with every other field in the data model. Anexample, data model 501 is shown in FIG. 5B. The data model 501illustrates connections between a plurality of tables which representlogical associations. Depending on the amount of data, the data model501 can be too large to be loaded into memory. To address this issue,the logical inference engine 106 can generate one or more indexes forthe data model. The one or more indexes can be loaded into memory inlieu of the data model 501. The one or more indexes can be used as theassociative model. An index is used by database management programs toprovide quick and efficient associative access to a table's records. Anindex is a data structure (for example, a B-tree, a hash table, and thelike) that stores attributes (e.g., values) for a specific column in atable. A B-tree is a self-balancing tree data structure that keeps datasorted and allows searches, sequential access, insertions, and deletionsin logarithmic time. The B-tree is a generalization of a binary searchtree in that a node can have more than two children. A hash table (alsoreferred to as a hash index) can comprise a collection of bucketsorganized in an array. A hash function maps index keys to correspondingbuckets in the hash index.

Queries that compare for equality to a string can retrieve values veryfast using a hash index. For instance, referring to the tables of FIG.2, a query of SELECT*FROM Table 2 WHERE Client=‘Kalle’ could benefitfrom a hash index created on the Client column. In this example, thehash index would be configured such that the column value will be thekey into the hash index and the actual value mapped to that key wouldjust be a pointer to the row data in Table 2. Since a hash index is anassociative array, a typical entry can comprise “Kalle=>0x29838”, where0x29838 is a reference to the table row where Kalle is stored in memory.Thus, looking up a value of “Kalle” in a hash index can return areference to the row in memory which is faster than scanning Table 2 tofind all rows with a value of “Kalle” in the Client column. The pointerto the row data enables retrieval of other values in the row.

As shown in FIG. 5B, the logical inference engine 106 can be configuredfor generating one or more bidirectional table indexes (BTI) 502 a, 502b, 502 c, 502 d, and/or 502 e and one or more bidirectional associativeindexes (BAI) 503 a. 503 b, 503 c and/or 503 d based on a data model501. The logical inference engine 106 can scan each table in the datamodel 501 and create the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e. ABTI can be created for each column of each table in the data. The BTI502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise a hash index. TheBTI 502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise firstattributes and pointers to the table rows comprising the firstattributes. For example, referring to the tables of FIG. 2, an exampleBTI 502 a can comprise “Kalle=>0x29838”, where Kalle is an attributefound in Table 2 and 0x29838 is a reference to the row in Table 2 whereKalle is stored in memory. Thus, the BTI 502 a, 502 b, 502 c, 502 d,and/or 502 e can be used to determine other attributes in other columns(e.g., second attributes, third attributes, etc. . . . ) in table rowscomprising the first attributes. Accordingly, the BTI can be used todetermine that an association exists between the first attributes andthe other attributes.

The logical inference engine 106 can scan one or more of BTI 502 a, 502b, 502 c, 502 d, and/or 502 e and create the BAI 503 a, 503 b, 503 cand/or 503 d. The BAI 503 a, 503 b, 503 c and/or 503 d can comprise ahash index. The BAI 503 a, 503 b, 503 c and/or 503 d can comprise anindex configured for connecting attributes in a first table to commoncolumns in a second table. The BAI 503 a. 503 b, 503 c and/or 503 d thusallows for identification of rows in the second table which then permitsidentification of other attributes in other tables. For example,referring to the tables of FIG. 2, an example BAI 503 a can comprise“Kalle=>0x39838”, where Kalle is an attribute found in Table 2 and0x39838 is a reference to a row in Table 4 that contains Kalle. In anaspect, the reference can be to a hash that can be in-memory or on disk.

Using the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e and the BAI 503a, 503 b, 503 c, and/or 503 d, the logical inference engine 106 cangenerate an index window 504 by taking a portion of the data model 501and mapping it into memory. The portion of the data model 501 taken intomemory can be sequential (e.g., not random). The result is a significantreduction in the size of data required to be loaded into memory.

Thus, the logical inference engine 106 can determine a data subset basedon user selections. The logical inference engine 106 automaticallymaintains associations among every piece of data in the entire data setused in an application. The logical inference engine 106 can store thebinary state of every field and of every data table dependent on userselection (e.g., included or excluded). This can be referred to as astate space and can be updated by the logical inference engine 106 everytime a selection is made. There is one bit in the state space for everyvalue in the symbol table or row in the data table, as such the statespace is smaller than the data itself and faster to query. The inferenceengine will work associating values or binary symbols into the dimensiontuples. Dimension tuples are normally needed by a hypercube to produce aresult.

The associations thus created by the logical inference engine 106 meansthat when a user makes a selection, the logical inference engine 106 canresolve (quickly) which values are still valid (e.g., possible values)and which values are excluded. The user can continue to make selections,clear selections, and make new selections, and the logical inferenceengine 106 will continue to present the correct results from the logicalinference of those selections. In contrast to a traditional join modeldatabase, the associative model provides an interactive associativeexperience to the user.

FIG. 5C, illustrates an example application of one or more BTIs. Userinput 504 can be received that impacts a selection of one or moreattribute states 506. Attribute states 506 can correspond to selectionby a user of one or more attributes (e.g., values) found in Column 1 ofTable X. In an aspect, the one or more attributes of Table X cancomprise a hash of each respective attribute. One or more BTI's 508 canbe accessed to determine one or more rows in Table X that comprise theattributes selected by the user. Row states 510 can correspond toselection of one or more rows found in Table X that comprise the one ormore selected attributes. An inverted index 512 of Column 2 can beaccessed to identify which rows of Table 1 comprise associatedattributes. Attribute states 514 for Column 2 can be updated to reflectthe associated attributes of Column 2. One or more BTI's 518 can befurther accessed to determine other associated attributes in othercolumns as needed. Attribute states 514 can be applied to other tablesvia one or more BAIs. FIG. 5D illustrates an example of relationshipsidentified by one or more BAIs.

FIG. 5E illustrates an example application of BTI's and BAI's todetermine inferred states both inter-table and intra-table using Table 1and Table 2 of FIG. 2. A BTI 520 can be generated for the “Client”attribute of Table 2. In an aspect, the BTI 520 can comprise an invertedindex 521. In other aspect, the inverted index 521 can be considered aseparate structure. The BTI 520 can comprise a row for each uniqueattribute in the “Client” column of Table 2. Each unique attribute canbe assigned a corresponding position 522 in the BTI 520. In an aspect,the BTI 520 can comprise a hash for each unique attribute. The BTI 520can comprise a column 523 for each row of Table 2. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 520 reflects that the attribute “Nisse” is found in rows 1 and 6of Table 2, the attribute “Gullan” is found in row 2 of Table 2, theattribute “Kalle” is found in rows 3 and 4 of Table 2, and the attribute“Pekka” is found in row 5 of Table 2.

The inverted index 521 can be generated such that each position in theinverted index 521 corresponds to a row of Table 2 (e.g., first positioncorresponds to row 1, second position corresponds to row 2, etc. . . .). A value can be entered into each position that reflects thecorresponding position 522 for each attribute. Thus, in the invertedindex 521, position 1 comprises the value “1” which is the correspondingposition 522 value for the attribute “Nisse”, position 2 comprises thevalue “2” which is the corresponding position 522 value for theattribute “Gullan”, position 3 comprises the value “3” which is thecorresponding position 522 value for the attribute “Kalle”, position 4comprises the value “3” which is the corresponding position 522 valuefor the attribute “Kalle”, position 5 comprises the value “4” which isthe corresponding position 522 value for the attribute “Pekka”, andposition 6 comprises the value “1” which is the corresponding position522 value for the attribute “Nisse”.

A BTI 524 can be generated for the “Product” attribute of Table 2. In anaspect, the BTI 524 can comprise an inverted index 525. In other aspect,the inverted index 525 can be considered a separate structure. The BTI524 can comprise a row for each unique attribute in the “Product” columnof Table 2. Each unique attribute can be assigned a correspondingposition 526 in the BTI 524. In an aspect, the BTI 524 can comprise ahash for each unique attribute. The BTI 524 can comprise a column 527for each row of Table 2. For each attribute, a “1” can indicate thepresence of the attribute in the row and a “0” can indicate an absenceof the attribute from the row. “0” and “1” are merely examples of valuesused to indicate presence or absence. Thus, the BTI 524 reflects thatthe attribute “Toothpaste” is found in row 1 of Table 2, the attribute“Soap” is found in rows 2, 3, and 5 of Table 2, and the attribute“Shampoo” is found in rows 4 and 6 of Table 2.

By way of example, the inverted index 525 can be generated such thateach position in the inverted index 525 corresponds to a row of Table 2(e.g., first position corresponds to row 1, second position correspondsto row 2, etc. . . . ). A value can be entered into each position thatreflects the corresponding position 526 for each attribute. Thus, in theinverted index 525, position 1 comprises the value “1” which is thecorresponding position 526 value for the attribute “Toothpaste”,position 2 comprises the value “2” which is the corresponding position526 value for the attribute “Soap”, position 3 comprises the value “2”which is the corresponding position 526 value for the attribute “Soap”,position 4 comprises the value “3” which is the corresponding position526 value for the attribute “Shampoo”, position 5 comprises the value“2” which is the corresponding position 526 value for the attribute“Soap”, and position 6 comprises the value “3” which is thecorresponding position 526 value for the attribute “Shampoo”.

By way of example, a BTI 528 can be generated for the “Product”attribute of Table 1. In an aspect, the BTI 528 can comprise an invertedindex 529. In other aspect, the inverted index 529 can be considered aseparate structure. The BTI 528 can comprise a row for each uniqueattribute in the “Product” column of Table 1. Each unique attribute canbe assigned a corresponding position 530 in the BTI 528. In an aspect,the BTI 528 can comprise a hash for each unique attribute. The BTI 528can comprise a column 531 for each row of Table 1. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 528 reflects that the attribute “Soap” is found in row 1 ofTable 1, the attribute “Soft Soap” is found in row 2 of Table 1, and theattribute “Toothpaste” is found in rows 3 and 4 of Table 1.

By way of example, the inverted index 529 can be generated such thateach position in the inverted index 529 corresponds to a row of Table 1(e.g., first position corresponds to row 1, second position correspondsto row 2, etc. . . . ). A value can be entered into each position thatreflects the corresponding position 530 for each attribute. Thus, in theinverted index 529, position 1 comprises the value “1” which is thecorresponding position 530 value for the attribute “Soap”, position 2comprises the value “2” which is the corresponding position 530 valuefor the attribute “Soft Soap”, position 3 comprises the value “3” whichis the corresponding position 530 value for the attribute “Toothpaste”,and position 4 comprises the value “3” which is the correspondingposition 530 value for the attribute “Toothpaste”.

By way of example, a BAI 532 can be generated as an index between theproduct attribute of Table 2 and Table 1. The BAI 532 can comprise a rowfor each unique attribute in the BTI 524 by order of correspondingposition 526. The value in each row can comprise the correspondingposition 530 of the BTI 528. Thus, position 1 of the BAI 532 correspondsto “Toothpaste” in the BTI 524 (corresponding position 526 of 1) andcomprises the value “3” which is the corresponding position 530 for“Toothpaste” of the BTI 528. Position 2 of the BAI 532 corresponds to“Soap” in the BTI 524 (corresponding position 526 of 2) and comprisesthe value “1” which is the corresponding position 530 for “Soap” of theBTI 528. Position 3 of the BAI 532 corresponds to “Shampoo” in the BTI524 (corresponding position 526 of 3) and comprises the value “−1” whichindicates that the attribute “Shampoo” is not found in Table 1.

By way of example, a BAI 533 can be created to create an index betweenthe product attribute of Table 1 and Table 2. The BAI 533 can comprise arow for each unique attribute in the BTI 528 by order of correspondingposition 530. The value in each row can comprise the correspondingposition 526 of the BTI 524.

Thus, position 1 of the BAI 533 corresponds to “Soap” in the BTI 528(corresponding position 530 of 1) and comprises the value “2” which isthe corresponding position 526 for “Soap” of the BTI 524. Position 2 ofthe BAI 533 corresponds to “Soft Soap” in the BTI 528 (correspondingposition 530 of 2) and comprises the value “−1” which indicates that theattribute “Soft Soap” is not found in Table 2. Position 3 of the BAI 533corresponds to “Toothpaste” in the BTI 528 (corresponding position 530of 3) and comprises the value “1” which is the corresponding position526 for “Toothpaste” of the BTI 524.

FIG. 5E illustrates an example application of the logical inferenceengine 106 utilizing the BTI 520, the BTI 524, and the BTI 528. A usercan select the “Client” “Kalle” from within a user interface. A columnfor a user selection 534 of “Kalle” can be indicated in the BTI 520comprising a value for each attribute that reflects the selection statusof the attribute. Thus, the user selection 534 comprises a value of “0”for the attribute “Nisse” indicating that “Nisse” is not selected, theuser selection 534 comprises a value of “0” for the attribute “Gullan”indicating that “Gullan” is not selected, the user selection 534comprises a value of “l” for the attribute “Kalle” indicating that“Kalle” is selected, and the user selection 534 comprises a value of “0”for the attribute “Pekka” indicating that “Pekka” is not selected.

The BTI 520 can be consulted to determine that the attribute “Kalle” hasa value of “1” in the column 523 corresponding to rows 3 and 4. In anaspect, the inverted index 521 can be consulted to determine that theuser selection 534 relates to the position 522 value of “3” which isfound in the inverted index 521 at positions 3 and 4, implicating rows 3and 4 of Table 1. Following path 535, a row state 536 can be generatedto reflect the user selection 534 as applied to the rows of Table 2. Therow state 536 can comprise a position that corresponds to each row and avalue in each position reflecting whether a row was selected. Thus,position 1 of the row state 536 comprises the value “0” indicating thatrow 1 does not contain “Kalle”, position 2 of the row state 536comprises the value “0” indicating that row 2 does not contain “Kalle”,position 3 of the row state 536 comprises the value “1” indicating thatrow 3 does contain “Kalle”, position 4 of the row state 536 comprisesthe value “1” indicating that row 4 does contain “Kalle”, position 5 ofthe row state 536 comprises the value “0” indicating that row 5 does notcontain “Kalle”, and position 6 of the row state 536 comprises the value“0” indicating that row 6 does not contain “Kalle”.

Following path 537, the row state 536 can be compared with the invertedindex 525 to determine the corresponding position 526 contained in theinverted index 525 at positions 3 and 4. The inverted index 525comprises the corresponding position 526 value of “2” in position 3 andthe corresponding position 526 value of “3” in position 4. Followingpath 538, the corresponding position 526 values of “2” and “3” can bedetermined to correspond to “Soap” and “Shampoo” respectively in the BTI524. Thus, the logical inference engine 106 can determine that both“Soap” and “Shampoo” in Table 2 are associated with “Kalle” in Table 2.The association can be reflected in an inferred state 539 in the BTI524. The inferred state 539 can comprise a column with a row for eachattribute in the BTI 524. The column can comprise a value indicated theselection state for each attribute. The inferred state 539 comprises a“0” for “Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”, the inferred state 539 comprises a “1” for “Soap” indicatingthat “Soap” is associated with “Kalle”, and inferred state 539 comprisesa “1” for “Shampoo” indicating that “Shampoo” is associated with“Kalle”.

Following path 540, the inferred state 539 can be compared to the BAI532 to determine one or more associations between the selection of“Kalle” in Table 2 and one or more attributes in Table 1. As theinferred state 539 comprises a value of “1” in both position 2 andposition 3, the BAI 532 can be assessed to determine the valuescontained in position 2 and position 3 of the BAI 532 (following path541). Position 2 of the BAI 532 comprises the value “1” which identifiesthe corresponding position 530 of “Soap” and position 3 of the BAI 532comprises the value “−1” which indicates that Table 1 does not contain“Shampoo”. Thus, the logical inference engine 106 can determine that“Soap” in Table 1 is associated with “Kalle” in Table 2. The associationcan be reflected in an inferred state 542 in the BTI 528. The inferredstate 542 can comprise a column with a row for each attribute in the BTI528. The column can comprise a value indicated the selection state foreach attribute. The inferred state 542 comprises a “1” for “Soap”indicating that “Soap” is associated with “Kalle”, the inferred state542 comprises a “0” for “Soft Soap” indicating that “Soft Soap” is notassociated with “Kalle”, and the inferred state 542 comprises a “0” for“Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”. Based on the current state of BTIs and BAIs, if the datasources 102 indicate that an update or delta change has occurred to theunderlying data, the BTIs and BAIs can be updated with correspondingchanges to maintain consistency.

Based on current selections and possible rows in data tables acalculation/chart engine 108 can calculate aggregations in objectsforming transient hyper cubes in an application. The calculation/chartengine 108 can further build a virtual temporary table from whichaggregations can be made. The calculation/chart engine 108 can perform acalculation (e.g., evaluate an expression in response to a userselection/de-selection) via a multithreaded operation. The state spacecan be queried to gather all of the combinations of dimensions andvalues necessary to perform the calculation. In an aspect, the query canbe on one thread per object, one process, one worker, combinationsthereof, and the like. The expression can be calculated on multiplethreads per object. Results of the calculation can be passed to arendering engine 116 and/or optionally to an extension engine 110.

Optionally, the extension engine 110 can be implemented to communicatedata via an interface 112 to an external engine 114. In another aspect,the extension engine 110 can communicate data, metadata, a script, areference to one or more artificial neural networks (ANNs), one or morecommands to be executed, one or more expressions to be evaluated,combinations thereof, and the like to the external engine 114. Theinterface 114 can comprise, for example, an Application ProgrammingInterface (API). The external engine 114 can comprise one or more dataprocessing applications (e.g., simulation applications, statisticalapplications, mathematical computation applications, databaseapplications, combinations thereof, and the like). The external engine114 can be, for example, one or more of MATLAB®, R, Maple®,Mathematica®, combinations thereof, and the like.

In an aspect, the external engine 114 can be local to the associativedata indexing engine 100 or the external engine 114 can be remote fromthe associative data indexing engine 100. The external engine 114 canperform additional calculations and transmit the results to theextension engine 110 via the interface 112. A user can make a selectionin the data model of data to be sent to the external engine 114. Thelogical inference engine 106 and/or the extension engine 110 cangenerate data to be output to the external engine 114 in a format towhich the external engine 114 is accustomed to processing. In an exampleapplication, tuples forming a hypercube can comprise two dimensions andone expression, such as (Month, Year, Count (ID)), ID being a recordidentification of one entry. Then said tuples can be exchanged with theexternal engine 114 through the interface 112 as a table. If the datacomprise births there can be timestamps of the births and these can bestored as month and year. If a selection in the data model will give aset of month-year values that are to be sent out to an external unit,the logical inference engine 106 and/or the extension engine 110 canripple that change to the data model associatively and produce the data(e.g., set and/or values) that the external engine 114 needs to workwith. The set and/or values can be exchanged through the interface 112with the external engine 114. The external engine 114 can comprise anymethod and/or system for performing an operation on the set and/orvalues. In an aspect, operations on the set and/or values by theexternal engine 114 can be based on tuples (aggregated or not). In anaspect, operations on the set and/or values by the external engine 114can comprise a database query based on the tuples. Operations on the setand/or values by the external engine 114 can be anytransformation/operation of the data as long as the cardinality of theresult is consonant to the sent tuples/hypercube result.

In an aspect, tuples that are transmitted to the external engine 114through the interface 112 can result in different data being receivedfrom the external engine 114 through the interface 112. For example, atuple consisting of (Month, Year, Count (ID)) should return as 1-to-1,m-to-1 (where aggregations are computed externally) or n-to-n values. Ifdata received are not what were expected, association can be lost.Transformation of data by the external engine 114 can be configured suchthat cardinality of the results is consonant to the sent tuples and/orhypercube results. The amount of values returned can thus preserveassociativity.

Results received by the extension engine 110 from the external engine114 can be appended to the data model. In an aspect, the data can beappended to the data model without intervention of the script engine104. Data model enrichment is thus possible “on the fly.” A natural workflow is available allowing clicking users to associatively extend thedata. The methods and systems disclosed permit incorporation of userimplemented functionality into a presently used work flow. Interactionwith third party complex computation engines, such as MATLAB® or R, isthus facilitated.

The logical inference engine 106 can couple associated results to theexternal engine 114 within the context of an already processed datamodel. The context can comprise tuple or tuples defined by dimensionsand expressions computed by hypercube routines. Association is used fordetermination of which elements of the present data model are relevantfor the computation at hand. Feedback from the external engine 114 canbe used for further inference inside the inference engine or to providefeedback to the user.

A rendering engine 116 can produce a desired graphical object (charts,tables, etc) based on selections/calculations. When a selection is madeon a rendered object there can be a repetition of the process of movingthrough one or more of the logical inference engine 106, thecalculation/chart engine 108, the extension engine 110, the externalengine 114, and/or the rendering engine 116. The user can explore thescope by making different selections, by clicking on graphical objectsto select variables, which causes the graphical object to change. Atevery time instant during the exploration, there exists a current statespace, which is associated with a current selection state that isoperated on the scope (which always remains the same).

Different export features or tools 118 can be used to publish, export ordeploy any output of the associative data indexing engine 100. Suchoutput can be any form of visual representation, including, but notlimited to, textual, graphical, animation, audio, tactile, and the like.

An example database, as shown in FIG. 2, can comprise a number of datatables (Tables 1-5). Each data table can contain data values of a numberof data variables. For example, in Table 1 each data record containsdata values of the data variables “Product,” “Price,” and “Part.” Ifthere is no specific value in a field of the data record, this field isconsidered to hold a NULL-value. Similarly, in Table 2 each data recordcontains values of the variables “Date,” “Client,” “Product,” and“Number.” In Table 3 each data record contains values of variable “Date”as “Year,” “Month” and “Day.” In Table 4 each data record containsvalues of variables “Client” and “Country,” and in Table 5 each datarecord contains values of variables “Country,” “Capital,” and“Population.” Typically, the data values are stored in the form ofASCII-coded strings, but can be stored in any form.

The methods provided can be implemented by means of a computer programas illustrated in a flowchart of a method 300 in FIG. 3. In a step 302,the program can read some or all data records in the database, forinstance using a SELECT statement which selects all the tables of thedatabase, e.g. Tables 1-5. In an aspect, the database can be read intoprimary memory of a computer.

To increase evaluation speed, each unique value of each data variable insaid database can be assigned a different binary code and the datarecords can be stored in binary-coded form. This can be performed, forexample, when the program first reads the data records from thedatabase. For each input table, the following steps can be carried out.The column names, e.g., the variables, of the table can be read (e.g.,successively). Every time a new data variable appears, a data structurecan be instantiated for the new data variable. An internal tablestructure can be instantiated to contain some or all the data records inbinary form, whereupon the data records can be read (e.g., successively)and binary-coded. For each data value, the data structure of thecorresponding data variable can be checked to establish if the value haspreviously been assigned a binary code. If so, that binary code can beinserted in the proper place in the above-mentioned table structure. Ifnot, the data value can be added to the data structure and assigned anew binary code, for example the next binary code in ascending order,before being inserted in the table structure. In other words, for eachdata variable, a unique binary code can be assigned to each unique datavalue.

After having read some or all data records in the database, the programcan analyze the database in a step 304 to identify all connectionsbetween the data tables. A connection between two data tables means thatthese data tables have one variable in common. In an aspect, step 304can comprise generation of one or more bidirectional table indexes andone or more bidirectional associative indexes. In an aspect, generationof one or more bidirectional table indexes and one or more bidirectionalassociative indexes can comprise a separate step. In another aspect,generation of one or more bidirectional table indexes and one or morebidirectional associative indexes can be on demand. After the analysis,all data tables are virtually connected. In FIG. 2, such virtualconnections are illustrated by double ended arrows. The virtuallyconnected data tables can form at least one so-called “snowflakestructure,” a branching data structure in which there is one and onlyone connecting path between any two data tables in the database. Thus, asnowflake structure does not contain any loops. If loops do occur amongthe virtually connected data tables, e.g. if two tables have more thanone variable in common, a snowflake structure can in some cases still beformed by means of special algorithms known in the art for resolvingsuch loops.

After this initial analysis, the user can explore the database. In doingso, the user defines in a step 306 a mathematical function, which couldbe a combination of mathematical expressions. Assume that the user wantsto extract the total sales per year and client from the database in FIG.2. The user defines a corresponding mathematical function “SUM (x*y)”,and selects the calculation variables to be included in this function:“Price” and “Number.” The user also selects the classificationvariables: “Client” and “Year.”

The computer program then identifies in a step 308 all relevant datatables, e.g. all data tables containing any one of the selectedcalculation and classification variables, such data tables being denotedboundary tables, as well as intermediate data tables in the connectingpath(s) between these boundary tables in the snowflake structure, suchdata tables being denoted connecting tables. There are no connectingtables in the present example. In an aspect, one or more bidirectionaltable indexes and one or more bidirectional associative indexes can beaccessed as part of step 308.

In the present example, all occurrences of every value, e.g. frequencydata, of the selected calculation variables can be included forevaluation of the mathematical function. In FIG. 2, the selectedvariables (“Price,” “Number”) can require such frequency data. Now, asubset (B) can be defined that includes all boundary tables (Tables 1-2)containing such calculation variables and any connecting tables betweensuch boundary tables in the snowflake structure. It should be noted thatthe frequency requirement of a particular variable is determined by themathematical expression in which it is included. Determination of anaverage or a median calls for frequency information. In general, thesame is true for determination of a sum, whereas determination of amaximum or a minimum does not require frequency data of the calculationvariables. It can also be noted that classification variables in generaldo not require frequency data.

Then, a starting table can be selected in a step 310, for example, amongthe data tables within subset (B). In an aspect, the starting table canbe the data table with the largest number of data records in thissubset. In FIG. 2, Table 2 can be selected as the starting table. Thus,the starting table contains selected variables (“Client,” “Number”), andconnecting variables (“Date,” “Product”). These connecting variableslink the starting table (Table 2) to the boundary tables (Tables 1 and3).

Thereafter, a conversion structure can be built in a step 312. Thisconversion structure can be used for translating each value of eachconnecting variable (“Date,” “Product”) in the starting table (Table 2)into a value of a corresponding selected variable (“Year,” “Price”) inthe boundary tables (Table 3 and 1, respectively). A table of theconversion structure can be built by successively reading data recordsof Table 3 and creating a link between each unique value of theconnecting variable (“Date”) and a corresponding value of the selectedvariable (“Year”). It can be noted that there is no link from value 4(“Date: 1999 Jan. 12”), since this value is not included in the boundarytable. Similarly, a further table of the conversion structure can bebuilt by successively reading data records of Table 1 and creating alink between each unique value of the connecting variable (“Product”)and a corresponding value of the selected variable (“Price”). In thisexample, value 2 (“Product: Toothpaste”) is linked to two values of theselected variable (“Price: 6.5”), since this connection occurs twice inthe boundary table. Thus, frequency data can be included in theconversion structure. Also note that there is no link from value 3(“Product: Shampoo”).

When the conversion structure has been built, a virtual data record canbe created. Such a virtual data record accommodates all selectedvariables (“Client,” “Year,” “Price,” “Number”) in the database. Inbuilding the virtual data record, a data record is read in a step 314from the starting table (Table 2). Then, the value of each selectedvariable (“Client”, “Number”) in the current data record of the startingtable can be incorporated in the virtual data record in a step 316.Also, by using the conversion structure each value of each connectingvariable (“Date”, “Product”) in the current data record of the startingtable can be converted into a value of a corresponding selected variable(“Year”, “Price”), this value also being incorporated in the virtualdata record.

In a step 318 the virtual data record can be used to build anintermediate data structure. Each data record of the intermediate datastructure can accommodate each selected classification variable(dimension) and an aggregation field for each mathematical expressionimplied by the mathematical function. The intermediate data structurecan be built based on the values of the selected variables in thevirtual data record. Thus, each mathematical expression can be evaluatedbased on one or more values of one or more relevant calculationvariables in the virtual data record, and the result can be aggregatedin the appropriate aggregation field based on the combination of currentvalues of the classification variables (“Client,” “Year”).

The above procedure can be repeated for one or more additional (e.g.,all) data records of the starting table. In a step 320 it can be checkedwhether the end of the starting table has been reached. If not, theprocess can be repeated from step 314 and further data records can beread from the starting table. Thus, an intermediate data structure canbe built by successively reading data records of the starting table, byincorporating the current values of the selected variables in a virtualdata record, and by evaluating each mathematical expression based on thecontent of the virtual data record. If the current combination of valuesof classification variables in the virtual data record is new, a newdata record can be created in the intermediate data structure to holdthe result of the evaluation. Otherwise, the appropriate data record israpidly found, and the result of the evaluation is aggregated in theaggregation field.

Thus, data records can be added to the intermediate data structure asthe starting table is traversed. The intermediate data structure can bea data table associated with an efficient index system, such as an AVLor a hash structure. The aggregation field can be implemented as asummation register, in which the result of the evaluated mathematicalexpression is accumulated.

In some aspects, e.g. when evaluating a median, the aggregation fieldcan be implemented to hold all individual results for a uniquecombination of values of the specified classification variables. Itshould be noted that only one virtual data record is needed in theprocedure of building the intermediate data structure from the startingtable. Thus, the content of the virtual data record can be updated foreach data record of the starting table. This can minimize the memoryrequirement in executing the computer program.

After traversing the starting table, the intermediate data structure cancontain a plurality of data records. If the intermediate data structureaccommodates more than two classification variables, the intermediatedata structure can, for each eliminated classification variable, containthe evaluated results aggregated over all values of this classificationvariable for each unique combination of values of remainingclassification variables.

When the intermediate data structure has been built, a final datastructure, e.g., a multidimensional cube, as shown in non-binarynotation in Table 6 of FIG. 4, can be created in a step 322 byevaluating the mathematical function (“SUM (x*y)”) based on the resultsof the mathematical expression (“x*y”) contained in the intermediatedata structure. In doing so, the results in the aggregation fields foreach unique combination of values of the classification variables can becombined. In the example, the creation of the final data structure isstraightforward, due to the trivial nature of the present mathematicalfunction. The content of the final data structure can be presented tothe user, for example in a two-dimensional table, in a step 324, asshown in Table 7 of FIG. 4. Alternatively, if the final data structurecontains many dimensions, the data can be presented in a pivot table, inwhich the user can interactively move up and down in dimensions, as iswell known in the art. At step 326, input from the user can be received.For example, input form the user can be a selection and/or de-selectionof the presented results.

Optionally, input from the user at step 326 can comprise a request forexternal processing. In an aspect, the user can be presented with anoption to select one or more external engines to use for the externalprocessing. Optionally, at step 328, data underlying the user selectioncan be configured (e.g., formatted) for use by an external engine.Optionally, at step 330, the data can be transmitted to the externalengine for processing and the processed data can be received. Thereceived data can undergo one or more checks to confirm that thereceived data is in a form that can be appended to the data model. Forexample, one or more of an integrity check, a format check, acardinality check, combinations thereof, and the like. Optionally, atstep 332, processed data can be received from the external engine andcan be appended to the data model as described herein. In an aspect, thereceived data can have a lifespan that controls how long the receiveddata persists with the data model. For example, the received data can beincorporated into the data model in a manner that enables a user toretrieve the received data at another time/session. In another example,the received data can persist only for the current session, making thereceived data unavailable in a future session.

FIG. 5A illustrates how a selection 50 operates on a scope 52 ofpresented data to generate a data subset 54. The data subset 54 can forma state space, which is based on a selection state given by theselection 50. In an aspect, the selection state (or “user state”) can bedefined by a user clicking on list boxes and graphs in a user interfaceof an application. An application can be designed to host a number ofgraphical objects (charts, tables, etc.) that evaluate one or moremathematical functions (also referred to as an “expression”) on the datasubset 54 for one or more dimensions (classification variables). Theresult of this evaluation creates a chart result 56 which can be amultidimensional cube which can be visualized in one or more of thegraphical objects.

The application can permit a user to explore the scope 52 by makingdifferent selections, by clicking on graphical objects to selectvariables, which causes the chart result 56 to change. At every timeinstant during the exploration, there exists a current state space,which can be associated with a current selection state that is operatedon the scope 52 (which always remains the same).

As illustrated in FIG. 5A, when a user makes a selection, the inferenceengine 18 calculates a data subset. Also, an identifier ID1 for theselection together with the scope can be generated based on the filtersin the selection and the scope. Subsequently, an identifier ID2 for thedata subset is generated based on the data subset definition, forexample a bit sequence that defines the content of the data subset. ID2can be put into a cache using ID1 as a lookup identifier. Likewise, thedata subset definition can be put in the cache using ID2 as a lookupidentifier.

As shown in FIG. 5A, a chart calculation in a calculation/chart engine58 takes place in a similar way. Here, there are two information sets:the data subset 54 and relevant chart properties 60. The latter can be,but not restricted to, a mathematical function together with calculationvariables and classification variables (dimensions). Both of theseinformation sets can be used to calculate the chart result 56, and bothof these information sets can be also used to generate identifier ID3for the input to the chart calculation. ID2 can be generated already inthe previous step, and ID3 can be generated as the first step in thechart calculation procedure.

The identifier ID3 can be formed from ID2 and the relevant chartproperties. ID3 can be seen as an identifier for a specific chartgeneration instance, which can include all information needed tocalculate a specific chart result. In addition, a chart resultidentifier ID4 can be created from the chart result definition, forexample a bit sequence that defines the chart result 56. ID4 can be putin the cache using ID3 as a lookup identifier. Likewise, the chartresult definition can be put in the cache using ID4 as a lookupidentifier.

Optionally, further calculations, transforming, and/or processing can beincluded through an extension engine 62. Optionally, associated resultsfrom the inference engine 18 and further computed by hypercubecomputation in said calculation/chart engine 58 can be coupled to anexternal engine 64 that can comprise one or more data processingapplications (e.g., simulation applications, statistical applications,mathematical computation applications, database applications,combinations thereof, and the like). Context of a data model processedby the inference engine 18 can comprise a tuple or tuples of valuesdefined by dimensions and expressions computed by hypercube routines.Data can be exchanged through an interface 66.

The associated results coupled to the external engine 64 can beintermediate. Further results that can be final hypercube results canalso be received from the external engine 64. Further results can be fedback to be included in the Data/Scope 52 and enrich the data model. Thefurther results can also be rendered directly to the user in the chartresult 56. Data received from and computed by the external engine 64 canbe used for further associative discovery.

Each of the data elements of the database shown in Tables 1-5 of FIG. 2has a data element type and a data element value (for example “Client”is the data element type and “Nisse” is the data element value).Multiple records can be stored in different database structures such asdata cubes, data arrays, data strings, flat files, lists, vectors, andthe like; and the number of database structures can be greater than orequal to one and can comprise multiple types and combinations ofdatabase structures. While these and other database structures can beused with, and as part of, the methods and systems disclosed, theremaining description will refer to tables, vectors, strings and datacubes solely for convenience.

Additional database structures can be included within the databaseillustrated as an example herein, with such structures includingadditional information pertinent to the database such as, in the case ofproducts for example; color, optional packages, etc. Each table cancomprise a header row which can identify the various data element types,often referred to as the dimensions or the fields, that are includedwithin the table. Each table can also have one or more additional rowswhich comprise the various records making up the table. Each of the rowscan contain data element values (including null) for the various dataelement types comprising the record.

The database as referred to in Tables 1-5 of FIG. 2 can be queried byspecifying the data element types and data element values of interestand by further specifying any functions to apply to the data containedwithin the specified data element types of the database. The functionswhich can be used within a query can include, for example, expressionsusing statistics, sub-queries, filters, mathematical formulas, and thelike, to help the user to locate and/or calculate the specificinformation wanted from the database. Once located and/or calculated,the results of a query can be displayed to the user with variousvisualization techniques and objects such as list boxes of a userinterface illustrated in FIG. 6.

The graphical objects (or visual representations) can be substantiallyany display or output type including graphs, charts, trees,multi-dimensional depictions, images (computer generated or digitalcaptures), video/audio displays describing the data, hybridpresentations where output is segmented into multiple display areashaving different data analysis in each area and so forth. A user canselect one or more default visual representations; however, a subsequentvisual representation can be generated on the basis of further analysisand subsequent dynamic selection of the most suitable form for the data.

In an aspect, a user can select a data point and a visualizationcomponent can instantaneously filter and re-aggregate other fields andcorresponding visual representations based on the user's selection. Inan aspect, the filtering and re-aggregation can be completed withoutquerying a database. In an aspect, a visual representation can bepresented to a user with color schemes applied meaningfully. Forexample, a user selection can be highlighted in green, datasets relatedto the selection can be highlighted in white, and unrelated data can behighlighted in gray. A meaningful application of a color scheme providesan intuitive navigation interface in the state space.

The result of a standard query can be a smaller subset of the datawithin the database, or a result set, which is comprised of the records,and more specifically, the data element types and data element valueswithin those records, along with any calculated functions, that matchthe specified query. For example, as indicated in FIG. 6, the dataelement value “Nisse” can be specified as a query or filtering criteriaas indicated by a frame in the “Client” header row. In some aspects, theselected element can be highlighted in green. By specifically selecting“Nisse,” other data element values in this row are excluded as shown bygray areas. Further, “Year” “1999” and “Month” “Jan” are selected in asimilar way.

Optionally, in this application, external processing can also berequested by ticking “External” in the user interface of FIG. 6. Data asshown in FIG. 7 can be exchanged with an External engine 64 through theinterface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM (Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical function (“SUM (ExtFunc(Price*Number))”) can beevaluated. Data sent out are (Nisse, 1999, Jan, {19.5, null}). In thiscase the external engine 64 can process data in accordance with theformula

  if (x==null)  y=0.5 else  y=xas shown in FIG. 7. The result input through the interface 66 will be(19.5, 0.5) as reflected in the graphical presentation in FIG. 6.

In a further aspect, external processing can also be optionallyrequested by ticking “External” in a box as shown in FIG. 8. Data asshown in FIG. 9 can be exchanged with an external engine 64 through theInterface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM(Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical function

SUM(ExtFunc(Price*Number))

can be evaluated. Data sent out are (Nisse, 1999, Jan, {19.5, null}). Inthis case the external engine 64 will process data in accordance withFunction (1) as shown below and in FIG. 9. The result input through theInterface 66 will be (61.5) as reflected in the graphical presentationin FIG. 8.

  Function (1) y=ExtAggr(x[ ])  for (x in x[ ])   if (x==null)    y=y +42     else      y=y+x

A further optional embodiment is shown in FIG. 10 and FIG. 11. The samebasic data as in previous examples apply. A user selects “Pekka,”“1999,” “Jan,” and “External.” By selecting “External,” alreadydetermined and associated results are coupled to the external engine 64.Feedback data from the external engine 64 based on an externalcomputation, ExtQualification(Sum(Price*Number)), as shown in FIG. 13will be the information “MVG.” This information can be fed back to thelogical inference engine 18. The information can also be fed back to thegraphical objects of FIG. 10 and as a result a qualification table 68will highlight “MVG” (illustrated with a frame in FIG. 10). Other values(U, G, and VG) are shown in gray areas. The result input through theInterface 66 will be Soap with a value of 75 as reflected in thegraphical presentation (bar chart) of FIG. 10. FIG. 11 is a schematicrepresentation of data exchanged with an external engine based onselections in FIG. 10. FIG. 12 is a table showing results fromcomputations based on different selections in the presentation of FIG.10.

Should a user instead select “Gullan,” “1999,” “Jan,” and “External,”the feedback signal would include “VG” based on the content shown inqualification table 68. The computations actually performed in theexternal engine 62 are not shown or indicated, since they are notrelevant to the inference engine.

In FIG. 13 a user has selected “G” as depicted by 70 in thequalification table 68. As a result information fed back from theexternal engine 64 to the external engine 62 and further to theinference engine 18 the following information will be highlighted:“Nisse,” “1999,” and “Jan” as shown in FIG. 13. Furthermore, the resultproduced will be Soap 37.5 as reflected in the graphical presentation(bar chart) of FIG. 13.

In an aspect, illustrated in FIG. 14 provided is a method 1400comprising determining one or more relationships between a plurality ofdata sets at 1410. Determining one or more relationships between aplurality of data sets can comprise determining a relationship R betweenany two data sets DS1 and DS2 by specifying a collection of one or morefields (DS1.F1, . . . DS1.Fn) and (DS2.F2, . . . DS2.Fk) from each dataset of the two data sets.

The method 1400 can comprise determining a score for each of the one ormore relationships at 1420. Determining the score for each of the one ormore relationships can comprise for a relationship R, determining anaggregate(P1, P2, . . . Pn), where Pk comprises aprofiling/classification parameter. The profiling/classificationparameter can comprise one or more of: one or more ratios of uniquevalues in one or more fields of a data set to a total number of rows inthe data set; one or more ratios of a size of an overlapping set ofvalues from one or more fields on both ends of an association betweendata sets to a number of unique values in the data sets; one or moreratios of a size of an overlapping set of values from fields on bothends of an association between data sets to a total number of rows inthe data sets; a history of user decisions made against the same orsimilar data; metadata supplied by an external system, a developer, oran administrator; a hint supplied by a developer or an administrator;and a classification score that identifies a potential lookup table.

The method 1400 can comprise generating a graphical data set object foreach of the plurality of data sets at 1430. A size of each graphicaldata set object can represent a number of rows in a data setcorresponding to the graphical data set object.

The method 1400 can comprise generating one or more graphical connectorobjects, wherein the one or more graphical connector objects connect oneof more of the graphical data set objects based on the determined one ormore relationships with a corresponding score above a threshold at 1440.

The method 1400 can comprise applying a force-directed graph layoutalgorithm to determine a position for each graphical data set objectbased on the scores for each of the one or more relationships at 1450.The method 1400 can comprise applying the force-directed graph layoutalgorithm to determine a distance between each of the graphical data setobjects based on the scores for each of the one or more relationships at1460. A position and a distance between two unconnected graphical dataset objects can represent a possible relationship between data setscorresponding to the two unconnected graphical data set objects.

The method 1400 can comprise outputting the graphical data set objectsand the one or more graphical connector objects based on the determinedpositions and distances at 1470. The method 1400 can further compriseoutputting a data model based on a connection state of the graphicaldata set objects.

The method 1400 can further comprise receiving a user interaction tomove one or more of the graphical data set objects and outputting thescores in response to the user interaction.

The method 1400 can further comprise receiving a user interaction toselect one of the graphical data set objects and outputting a preview ofa data set corresponding to the selected graphical data set object inresponse to the user interaction.

The method 1400 can further comprise receiving a user interaction toselect one of the graphical connector objects and outputting a previewof a relationship corresponding to the selected graphical connectorobject in response to the user interaction.

The method 1400 can further comprise receiving a user interaction toselect one of the graphical connector objects and outputting a list ofrecommended alternatives for the relationship corresponding to theselected graphical connector object.

The method 1400 can further comprise receiving a user interaction tomove a first graphical data set object away from a second graphical dataset object and towards a third graphical data set object, removing afirst graphical connector object connecting the first graphical data setobject and the second graphical data set object, and generating a secondgraphical connector object between the first graphical data set objectand the third graphical data set object, based on the determined scorefor the relationship between the first graphical data set object and thethird graphical data set object.

In an aspect, illustrated in FIG. 15 provided is a method 1500comprising determining one or more relationships between a plurality ofdata sets at 1510. Determining one or more relationships between aplurality of data sets can comprise determining a relationship R betweenany two data sets DS1 and DS2 by specifying a collection of one or morefields (DS1.F1, . . . DS1.Fn) and (DS2.F2, . . . DS2.Fk) from each dataset of the two data sets.

The method 1500 can comprise determining a score for each of the one ormore relationships at 1520. Determining the score for each of the one ormore relationships can comprise for a relationship R, determining anaggregate(P1, P2, . . . Pn), where Pk comprises aprofiling/classification parameter. The profiling/classificationparameter can comprise one or more of one or more ratios of uniquevalues in one or more fields of a data set to a total number of rows inthe data set; one or more ratios of a size of an overlapping set ofvalues from one or more fields on both ends of an association betweendata sets to a number of unique values in the data sets; one or moreratios of a size of an overlapping set of values from fields on bothends of an association between data sets to a total number of rows inthe data sets; a history of user decisions made against the same orsimilar data; metadata supplied by an external system, a developer, oran administrator; a hint supplied by a developer or an administrator;and a classification score that identifies a potential lookup table.

The method 1500 can comprise generating a graphical data set object foreach of the plurality of data sets at 1530. A size of each graphicaldata set object can represent a number of rows in a data setcorresponding to the graphical data set object.

The method 1500 can comprise classifying each graphical data set objectas connected or unconnected based on the score for each of the one ormore relationships at 1540. A graphical data set object can beclassified as connected if the corresponding data set object has arelationship with another data set with a score above a threshold.

The method 1500 can comprise generating a graphical connector objectbetween connected graphical data set objects at 1550.

The method 1500 can comprise outputting the connected graphical data setobjects with corresponding graphical connector objects and theunconnected graphical data set objects at 1560. Outputting the connectedgraphical data set objects with corresponding graphical connectorobjects and the unconnected graphical data set objects can comprisedetermining a position for each graphical data set object based on thescores for each of the one or more relationships, determining a distancebetween each of the graphical data set objects based on the scores foreach of the one or more relationships, and outputting the graphical dataset objects and the one or more graphical connector objects based on thedetermined positions and distances.

The method 1500 can further comprise outputting a data model based on aconnection state of the graphical data set objects.

The method 1500 can further comprise receiving one or more userinteractions that change a connection state of the connected graphicaldata set objects and the unconnected graphical data set objects andoutputting a data model based on the connection state of the graphicaldata set objects.

In an exemplary aspect, the methods and systems can be implemented on acomputer 1601 as illustrated in FIG. 16 and described below. Similarly,the methods and systems disclosed can utilize one or more computers toperform one or more functions in one or more locations. FIG. 16 is ablock diagram illustrating an exemplary operating environment forperforming the disclosed methods. This exemplary operating environmentis only an example of an operating environment and is not intended tosuggest any limitation as to the scope of use or functionality ofoperating environment architecture. Neither should the operatingenvironment be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplar) operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 1601. The components of thecomputer 1601 can comprise, but are not limited to, one or moreprocessors 1603, a system memory 1612, and a system bus 1613 thatcouples various system components including the one or more processors1603 to the system memory 1612. The system can utilize parallelcomputing.

The system bus 1613 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, or local bus using any ofa variety of bus architectures. The bus 1613, and all buses specified inthis description can also be implemented over a wired or wirelessnetwork connection and each of the subsystems, including the one or moreprocessors 1603, a mass storage device 1604, an operating system 1605,interactive discovery software 1606, data 1607, a network adapter 1608,the system memory 1612, an Input/Output Interface 1610, a displayadapter 1609, a display device 1611, and a human machine interface 1602,can be contained within one or more remote computing devices 1614 a,b,cat physically separate locations, connected through buses of this form,in effect implementing a fully distributed system.

The computer 1601 typically comprises a variety of computer readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 1601 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 1612 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 1612 typically contains data such as the data 1607 and/orprogram modules such as the operating system 1605 and the interactivediscovery software 1606 that are immediately accessible to and/or arepresently operated on by the one or more processors 1603.

In another aspect, the computer 1601 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, FIG. 16 illustrates the mass storage device 1604which can provide non-volatile storage of computer code, computerreadable instructions, data structures, program modules, and other datafor the computer 1601. For example and not meant to be limiting, themass storage device 1604 can be a hard disk, a removable magnetic disk,a removable optical disk, magnetic cassettes or other magnetic storagedevices, flash memory cards, CD-ROM, digital versatile disks (DVD) orother optical storage, random access memories (RAM), read only memories(ROM), electrically erasable programmable read-only memory (EEPROM), andthe like.

Optionally, any number of program modules can be stored on the massstorage device 1604, including by way of example, the operating system1605 and the interactive discovery software 1606. Each of the operatingsystem 1605 and the interactive discovery software 1606 (or somecombination thereof) can comprise elements of the programming and theinteractive discovery software 1606. The data 1607 can also be stored onthe mass storage device 1604. The data 1607 can be stored in any of oneor more databases known in the art. Examples of such databases comprise,DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL,PostgreSQL, and the like. The databases can be centralized ordistributed across multiple systems.

In an aspect, the interactive discovery software 1606 can comprise oneor more of a script engine, a logical inference engine, a calculationengine, an extension engine, and/or a rendering engine. In an aspect,the interactive discovery software 1606 can comprise an external engineand/or an interface to the external engine.

In another aspect, the user can enter commands and information into thecomputer 1601 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like. Theseand other input devices can be connected to the one or more processors1603 via the human machine interface 1602 that is coupled to the systembus 1613, but can be connected by other interface and bus structures,such as a parallel port, game port, an IEEE 1394 Port (also known as aFirewire port), a serial port, or a universal serial bus (USB).

In yet another aspect, the display device 1611 can also be connected tothe system bus 1613 via an interface, such as the display adapter 1609.It is contemplated that the computer 1601 can have more than one displayadapter 1609 and the computer 1601 can have more than one display device1611. For example, the display device 1611 can be a monitor, an LCD(Liquid Crystal Display), or a projector. In addition to the displaydevice 1611, other output peripheral devices can comprise componentssuch as speakers (not shown) and a printer (not shown) which can beconnected to the computer 1601 via the Input/Output Interface 1610. Anystep and/or result of the methods can be output in any form to an outputdevice. Such output can be any form of visual representation, including,but not limited to, textual, graphical, animation, audio, tactile, andthe like. The display device 1611 and computer 1601 can be part of onedevice, or separate devices.

The computer 1601 can operate in a networked environment using logicalconnections to one or more remote computing devices 1614 a,b,c. By wayof example, a remote computing device can be a personal computer,portable computer, smartphone, a server, a router, a network computer, apeer device or other common network node, and so on. Logical connectionsbetween the computer 1601 and a remote computing device 1614 a,b,c canbe made via a network 1615, such as a local area network (LAN) and/or ageneral wide area network (WAN). Such network connections can be throughthe network adapter 1608. The network adapter 1608 can be implemented inboth wired and wireless environments. Such networking environments areconventional and commonplace in dwellings, offices, enterprise-widecomputer networks, intranets, and the Internet. In an aspect, one ormore of the remote computing devices 1614 a,b,c can comprise an externalengine and/or an interface to the external engine.

For purposes of illustration, application programs and other executableprogram components such as the operating system 1605 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 1601, and are executed by the one or moreprocessors 1603 of the computer. An implementation of the interactivediscovery software 1606 can be stored on or transmitted across some formof computer readable media. Any of the disclosed methods can beperformed by computer readable instructions embodied on computerreadable media. Computer readable media can be any available media thatcan be accessed by a computer. By way of example and not meant to belimiting, computer readable media can comprise “computer storage media”and “communications media.” “Computer storage media” comprise volatileand non-volatile, removable and non-removable media implemented in anymethods or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Exemplary computer storage media comprises, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by a computer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case basedreasoning, Bayesian networks, behavior based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is in no way intendedthat an order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including; matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

What is claimed:
 1. A method comprising: determining one or morerelationships between a plurality of data sets; determining a score foreach of the one or more relationships; generating a graphical data setobject for each of the plurality of data sets; generating one or moregraphical connector objects, wherein the one or more graphical connectorobjects connect one of more of the graphical data set objects based onthe determined one or more relationships with a corresponding scoreabove a threshold; applying a force-directed graph layout algorithm todetermine a position for each graphical data set object based on thescores for each of the one or more relationships; applying theforce-directed graph layout algorithm to determine a distance betweeneach of the graphical data set objects based on the scores for each ofthe one or more relationships; and outputting the graphical data setobjects and the one or more graphical connector objects based on thedetermined positions and distances.
 2. The method of claim 1, whereindetermining one or more relationships between a plurality of data setscomprises determining a relationship R between any two data sets DS1 andDS2 by specifying a collection of one or more fields (DS1.F1, . . .DS1.Fn) and (DS2.F2, . . . DS2.Fk) from each data set of the two datasets.
 3. The method of claim 2, wherein determining the score for eachof the one or more relationships comprises for a relationship R,determining an aggregate(P1, P2, . . . Pn), where Pk comprises aprofiling/classification parameter.
 4. The method of claim 3, whereinthe profiling/classification parameter comprises one or more of: one ormore ratios of unique values in one or more fields of a data set to atotal number of rows in the data set; one or more ratios of a size of anoverlapping set of values from one or more fields on both ends of anassociation between data sets to a number of unique values in the datasets; one or more ratios of a size of an overlapping set of values fromfields on both ends of an association between data sets to a totalnumber of rows in the data sets; a history of user decisions madeagainst the same or similar data; metadata supplied by an externalsystem, a developer, or an administrator; a hint supplied by a developeror an administrator; and a classification score that identifies apotential lookup table.
 5. The method of claim 1, wherein a size of eachgraphical data set object represents a number of rows in a data setcorresponding to the graphical data set object.
 6. The method of claim1, wherein a position and a distance between two unconnected graphicaldata set objects represents a possible relationship between data setscorresponding to the two unconnected graphical data set objects.
 7. Themethod of claim 1, further comprising outputting a data model based on aconnection state of the graphical data set objects.
 8. The method ofclaim 1, further comprising: receiving a user interaction to move one ormore of the graphical data set objects; and outputting the scores inresponse to the user interaction.
 9. The method of claim 1, furthercomprising: receiving a user interaction to select one of the graphicaldata set objects; and outputting a preview of a data set correspondingto the selected graphical data set object in response to the userinteraction.
 10. The method of claim 1, further comprising: receiving auser interaction to select one of the graphical connector objects; andoutputting a preview of a relationship corresponding to the selectedgraphical connector object in response to the user interaction.
 11. Themethod of claim 1, further comprising: receiving a user interaction toselect one of the graphical connector objects; and outputting a list ofrecommended alternatives for the relationship corresponding to theselected graphical connector object.
 12. The method of claim 1, furthercomprising: receiving a user interaction to move a first graphical dataset object away from a second graphical data set object and towards athird graphical data set object; removing a first graphical connectorobject connecting the first graphical data set object and the secondgraphical data set object; and generating a second graphical connectorobject between the first graphical data set object and the thirdgraphical data set object, based on the determined score for therelationship between the first graphical data set object and the thirdgraphical data set object.
 13. The method of claim 12, furthercomprising outputting a data model based on a connection state of thegraphical data set objects.
 14. A method comprising: determining one ormore relationships between a plurality of data sets; determining a scorefor each of the one or more relationships; generating a graphical dataset object for each of the plurality of data sets; classifying eachgraphical data set object as connected or unconnected based on the scorefor each of the one or more relationships; generating a graphicalconnector object between connected graphical data set objects; andoutputting the connected graphical data set objects with correspondinggraphical connector objects and the unconnected graphical data setobjects.
 15. The method of claim 14, wherein determining one or morerelationships between a plurality of data sets comprises determining arelationship R between any two data sets DS1 and DS2 by specifying acollection of one or more fields (DS1.F1, . . . DS1.Fn) and (DS2.F2, . .. DS2.Fk) from each data set of the two data sets.
 16. The method ofclaim 15, wherein determining the score for each of the one or morerelationships comprises for a relationship R, determining anaggregate(P1, P2, . . . Pn), where Pk comprises aprofiling/classification parameter.
 17. The method of claim 16, whereinthe profiling/classification parameter comprises one or more of: one ormore ratios of unique values in one or more fields of a data set to atotal number of rows in the data set; one or more ratios of a size of anoverlapping set of values from one or more fields on both ends of anassociation between data sets to a number of unique values in the datasets; one or more ratios of a size of an overlapping set of values fromfields on both ends of an association between data sets to a totalnumber of rows in the data sets; a history of user decisions madeagainst the same or similar data; metadata supplied by an externalsystem, a developer, or an administrator; a hint supplied by a developeror an administrator; and a classification score that identifies apotential lookup table.
 18. The method of claim 14, further comprisingoutputting a data model based on a connection state of the graphicaldata set objects.
 19. The method of claim 14, wherein a size of eachgraphical data set object represents a number of rows in a data setcorresponding to the graphical data set object.
 20. The method of claim14, wherein a graphical data set object is classified as connected ifthe corresponding data set object has a relationship with another dataset with a score above a threshold.
 21. The method of claim 14, whereinoutputting the connected graphical data set objects with correspondinggraphical connector objects and the unconnected graphical data setobjects comprises; determining a position for each graphical data setobject based on the scores for each of the one or more relationships;determining a distance between each of the graphical data set objectsbased on the scores for each of the one or more relationships; andoutputting the graphical data set objects and the one or more graphicalconnector objects based on the determined positions and distances. 22.The method of claim 14, further comprising: receiving one or more userinteractions that change a connection state of the connected graphicaldata set objects and the unconnected graphical data set objects; andoutputting a data model based on the connection state of the graphicaldata set objects.